Nonlinear Modeling of Solar Radiation and Wind Speed Time Series (SpringerBriefs in Energy)

This brief is a clear, concise description of the main techniques of time series analysis --stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.-- as they are applied to the influence of wind speed and solar radiation on the production of electrical energy from these renewable sources. The problem of implementing prediction models is addressed by using the embedding-phase-space approach: a powerful technique for the modeling of complex systems. Readers are also guided in applying the main machine learning techniques for classification of the patterns hidden in their time series and so will be able to perform statistical analyses that are not possible by using conventional techniques. The conceptual exposition avoids unnecessary mathematical details and focuses on concrete examples in order to ensure a better understanding of the proposed techniques. Results are well-illustrated by figures and tables Front Matter....Pages i-xv Time Series Methods....Pages 1-15 Analysis of Solar Radiation Time Series....Pages 17-27 Analysis of Wind Speed Time Series....Pages 29-40 Prediction Models for Solar Radiation and Wind Speed Time Series....Pages 41-46 Modeling Hourly Average Solar Radiation Time Series....Pages 47-59 Modeling Hourly Average Wind Speed Time Series....Pages 61-67 Clustering Daily Solar Radiation Time Series....Pages 69-78 Clustering Daily Wind Speed Time Series....Pages 79-89 Concluding Remarks....Pages 91-92 Back Matter....Pages 93-98 MATLAB (R) funtions implemented on time-series analysis and modeling as described in this book are available for download. Detailed instructions are provided to help the user employ the code correctly.